Kaisa Miettinen

Nonlinear Multiobjective Optimization

• Gebundenes Buch

Produktbeschreibung

Problems with multiple objectives and criteria are generally known as multiple criteria optimization or multiple criteria decision-making (MCDM) problems. So far, these types of problems have typically been modelled and solved by means of linear programming. However, many real-life phenomena are of a nonlinear nature, which is why we need tools for nonlinear programming capable of handling several conflicting or incommensurable objectives. In this case, methods of traditional single objective optimization and linear programming are not enough; we need new ways of thinking, new concepts, and new methods - nonlinear multiobjective optimization.
Nonlinear Multiobjective Optimization provides an extensive, up-to-date, self-contained and consistent survey, review of the literature and of the state of the art on nonlinear (deterministic) multiobjective optimization, its methods, its theory and its background. The amount of literature on multiobjective optimization is immense. The treatment in this book is based on approximately 1500 publications in English printed mainly after the year 1980.
Problems related to real-life applications often contain irregularities and nonsmoothnesses. The treatment of nondifferentiable multiobjective optimization in the literature is rather rare. For this reason, this book contains material about the possibilities, background, theory and methods of nondifferentiable multiobjective optimization as well.
This book is intended for both researchers and students in the areas of (applied) mathematics, engineering, economics, operations research and management science; it is meant for both professionals and practitioners in many different fields of application. The intention has been to provide a consistent summary that may help in selecting an appropriate method for the problem to be solved. It is hoped the extensive bibliography will be of value to researchers.

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Produktdetails

• International Series in Operations Research & Management Science 12
• Verlag: Springer / Springer US / Springer, Berlin
• Artikelnr. des Verlages: 978-0-7923-8278-2
• 1998.
• Seitenzahl: 324
• Erscheinungstermin: 30. September 1998
• Englisch
• Abmessung: 241mm x 160mm x 24mm
• Gewicht: 630g
• ISBN-13: 9780792382782
• ISBN-10: 0792382781
• Artikelnr.: 22423425

Inhaltsangabe

I Terminology And Theory.- 2. Concepts.- 3. Theoretical Background.- II Methods.- 2. No-Preference Methods.- 3. A Posteriori Methods.- 4. A Priori Methods.- 5. Interactive Methods.- III Related Issues.- 1. Comparing Methods.- 2. Software.- 3. Graphical Illustration.- 4. Future Directions.- 5. Epilogue.- References.

Rezensionen

`Overall, I find this book to be a valuable addition to my library of references on MCDM problems. I recommend this book to anyone interested in learning about nonlinear multiple objective programming methods, to anyone interested in choosing one of these methods to solve practical problems, and to researchers seeking to create more useful nonlinear multiple objective programming algorithms.'
Harold P. Benson, University of Florida
`As recent books on multiobjective optimization are scarce, Miettinen's book definitely fills a gap in this area and is definitely recommended for all interested readers of different degrees of familiarity with nonlinear multiobjective optimization.'
Serpil Sayin, Koç University

`Overall, I find this book to be a valuable addition to my library of references on MCDM problems. I recommend this book to anyone interested in learning about nonlinear multiple objective programming methods, to anyone interested in choosing one of these methods to solve practical problems, and to researchers seeking to create more useful nonlinear multiple objective programming algorithms.' Harold P. Benson, University of Florida `As recent books on multiobjective optimization are scarce, Miettinen's book definitely fills a gap in this area and is definitely recommended for all interested readers of different degrees of familiarity with nonlinear multiobjective optimization.' Serpil Sayin, Koç University